e2e-validation.test.ts•13.7 kB
/**
* Integration Tests: End-to-End AI Workflow Validation
*
* Tests complete AI workflow validation and creation flow.
* Validates multi-error detection and workflow creation after validation.
*/
import { describe, it, expect, beforeEach, afterEach, afterAll } from 'vitest';
import { createTestContext, TestContext, createTestWorkflowName } from '../n8n-api/utils/test-context';
import { getTestN8nClient } from '../n8n-api/utils/n8n-client';
import { N8nApiClient } from '../../../src/services/n8n-api-client';
import { cleanupOrphanedWorkflows } from '../n8n-api/utils/cleanup-helpers';
import { createMcpContext } from '../n8n-api/utils/mcp-context';
import { InstanceContext } from '../../../src/types/instance-context';
import { handleValidateWorkflow, handleCreateWorkflow } from '../../../src/mcp/handlers-n8n-manager';
import { getNodeRepository, closeNodeRepository } from '../n8n-api/utils/node-repository';
import { NodeRepository } from '../../../src/database/node-repository';
import { ValidationResponse } from '../n8n-api/types/mcp-responses';
import {
createChatTriggerNode,
createAIAgentNode,
createLanguageModelNode,
createHTTPRequestToolNode,
createCodeToolNode,
createMemoryNode,
createRespondNode,
createAIConnection,
createMainConnection,
mergeConnections,
createAIWorkflow
} from './helpers';
describe('Integration: End-to-End AI Workflow Validation', () => {
let context: TestContext;
let client: N8nApiClient;
let mcpContext: InstanceContext;
let repository: NodeRepository;
beforeEach(async () => {
context = createTestContext();
client = getTestN8nClient();
mcpContext = createMcpContext();
repository = await getNodeRepository();
});
afterEach(async () => {
await context.cleanup();
});
afterAll(async () => {
await closeNodeRepository();
if (!process.env.CI) {
await cleanupOrphanedWorkflows();
}
});
// ======================================================================
// TEST 1: Validate and Create Complex AI Workflow
// ======================================================================
it('should validate and create complex AI workflow', async () => {
const chatTrigger = createChatTriggerNode({
name: 'Chat Trigger',
responseMode: 'lastNode'
});
const languageModel = createLanguageModelNode('openai', {
name: 'OpenAI Chat Model'
});
const httpTool = createHTTPRequestToolNode({
name: 'Weather API',
toolDescription: 'Fetches current weather data from weather API',
url: 'https://api.weather.com/current',
method: 'GET'
});
const codeTool = createCodeToolNode({
name: 'Data Processor',
toolDescription: 'Processes and formats weather data',
code: 'return { formatted: JSON.stringify($input.all()) };'
});
const memory = createMemoryNode({
name: 'Conversation Memory',
contextWindowLength: 10
});
const agent = createAIAgentNode({
name: 'Weather Assistant',
promptType: 'define',
text: 'You are a weather assistant. Help users understand weather data.',
systemMessage: 'You are an AI assistant specialized in weather information. You have access to weather APIs and can process data. Always provide clear, helpful responses.'
});
const respond = createRespondNode({
name: 'Respond to User'
});
const workflow = createAIWorkflow(
[chatTrigger, languageModel, httpTool, codeTool, memory, agent, respond],
mergeConnections(
createMainConnection('Chat Trigger', 'Weather Assistant'),
createAIConnection('OpenAI Chat Model', 'Weather Assistant', 'ai_languageModel'),
createAIConnection('Weather API', 'Weather Assistant', 'ai_tool'),
createAIConnection('Data Processor', 'Weather Assistant', 'ai_tool'),
createAIConnection('Conversation Memory', 'Weather Assistant', 'ai_memory'),
createMainConnection('Weather Assistant', 'Respond to User')
),
{
name: createTestWorkflowName('E2E - Complex AI Workflow'),
tags: ['mcp-integration-test', 'ai-validation', 'e2e']
}
);
// Step 1: Create workflow
const created = await client.createWorkflow(workflow);
context.trackWorkflow(created.id!);
// Step 2: Validate workflow
const validationResponse = await handleValidateWorkflow(
{ id: created.id },
repository,
mcpContext
);
expect(validationResponse.success).toBe(true);
const validationData = validationResponse.data as ValidationResponse;
// Workflow should be valid
expect(validationData.valid).toBe(true);
expect(validationData.errors).toBeUndefined();
expect(validationData.summary.errorCount).toBe(0);
// Verify all nodes detected
expect(validationData.summary.totalNodes).toBe(7);
expect(validationData.summary.triggerNodes).toBe(1);
// Step 3: Since it's valid, it's already created and ready to use
// Just verify it exists
const retrieved = await client.getWorkflow(created.id!);
expect(retrieved.id).toBe(created.id);
expect(retrieved.nodes.length).toBe(7);
});
// ======================================================================
// TEST 2: Detect Multiple Validation Errors
// ======================================================================
it('should detect multiple validation errors', async () => {
const chatTrigger = createChatTriggerNode({
name: 'Chat Trigger',
responseMode: 'streaming'
});
const httpTool = createHTTPRequestToolNode({
name: 'HTTP Tool',
toolDescription: '', // ERROR: missing description
url: '', // ERROR: missing URL
method: 'GET'
});
const codeTool = createCodeToolNode({
name: 'Code Tool',
toolDescription: 'Short', // WARNING: too short
code: '' // ERROR: missing code
});
const agent = createAIAgentNode({
name: 'AI Agent',
promptType: 'define',
text: '', // ERROR: missing prompt text
// ERROR: missing language model connection
// ERROR: has main output in streaming mode
});
const respond = createRespondNode({
name: 'Respond'
});
const workflow = createAIWorkflow(
[chatTrigger, httpTool, codeTool, agent, respond],
mergeConnections(
createMainConnection('Chat Trigger', 'AI Agent'),
createAIConnection('HTTP Tool', 'AI Agent', 'ai_tool'),
createAIConnection('Code Tool', 'AI Agent', 'ai_tool'),
createMainConnection('AI Agent', 'Respond') // ERROR in streaming mode
),
{
name: createTestWorkflowName('E2E - Multiple Errors'),
tags: ['mcp-integration-test', 'ai-validation', 'e2e']
}
);
const created = await client.createWorkflow(workflow);
context.trackWorkflow(created.id!);
const validationResponse = await handleValidateWorkflow(
{ id: created.id },
repository,
mcpContext
);
expect(validationResponse.success).toBe(true);
const validationData = validationResponse.data as ValidationResponse;
// Should be invalid with multiple errors
expect(validationData.valid).toBe(false);
expect(validationData.errors).toBeDefined();
expect(validationData.errors!.length).toBeGreaterThan(3);
// Verify specific errors are detected
const errorCodes = validationData.errors!.map(e => e.details?.code || e.code);
expect(errorCodes).toContain('MISSING_LANGUAGE_MODEL'); // AI Agent
expect(errorCodes).toContain('MISSING_PROMPT_TEXT'); // AI Agent
expect(errorCodes).toContain('MISSING_TOOL_DESCRIPTION'); // HTTP Tool
expect(errorCodes).toContain('MISSING_URL'); // HTTP Tool
expect(errorCodes).toContain('MISSING_CODE'); // Code Tool
// Should also have streaming error
const streamingErrors = validationData.errors!.filter(e => {
const code = e.details?.code || e.code;
return code === 'STREAMING_WITH_MAIN_OUTPUT' ||
code === 'STREAMING_AGENT_HAS_OUTPUT';
});
expect(streamingErrors.length).toBeGreaterThan(0);
// Verify error messages are actionable
for (const error of validationData.errors!) {
expect(error.message).toBeDefined();
expect(error.message.length).toBeGreaterThan(10);
expect(error.nodeName).toBeDefined();
}
});
// ======================================================================
// TEST 3: Validate Streaming Workflow (No Main Output)
// ======================================================================
it('should validate streaming workflow without main output', async () => {
const chatTrigger = createChatTriggerNode({
name: 'Chat Trigger',
responseMode: 'streaming'
});
const languageModel = createLanguageModelNode('anthropic', {
name: 'Claude Model'
});
const agent = createAIAgentNode({
name: 'Streaming Agent',
text: 'You are a helpful assistant',
systemMessage: 'Provide helpful, streaming responses to user queries'
});
const workflow = createAIWorkflow(
[chatTrigger, languageModel, agent],
mergeConnections(
createMainConnection('Chat Trigger', 'Streaming Agent'),
createAIConnection('Claude Model', 'Streaming Agent', 'ai_languageModel')
// No main output from agent - streaming mode
),
{
name: createTestWorkflowName('E2E - Streaming Workflow'),
tags: ['mcp-integration-test', 'ai-validation', 'e2e']
}
);
const created = await client.createWorkflow(workflow);
context.trackWorkflow(created.id!);
const validationResponse = await handleValidateWorkflow(
{ id: created.id },
repository,
mcpContext
);
expect(validationResponse.success).toBe(true);
const validationData = validationResponse.data as ValidationResponse;
expect(validationData.valid).toBe(true);
expect(validationData.errors).toBeUndefined();
expect(validationData.summary.errorCount).toBe(0);
});
// ======================================================================
// TEST 4: Validate Non-Streaming Workflow (With Main Output)
// ======================================================================
it('should validate non-streaming workflow with main output', async () => {
const chatTrigger = createChatTriggerNode({
name: 'Chat Trigger',
responseMode: 'lastNode'
});
const languageModel = createLanguageModelNode('openai', {
name: 'GPT Model'
});
const agent = createAIAgentNode({
name: 'Non-Streaming Agent',
text: 'You are a helpful assistant'
});
const respond = createRespondNode({
name: 'Final Response'
});
const workflow = createAIWorkflow(
[chatTrigger, languageModel, agent, respond],
mergeConnections(
createMainConnection('Chat Trigger', 'Non-Streaming Agent'),
createAIConnection('GPT Model', 'Non-Streaming Agent', 'ai_languageModel'),
createMainConnection('Non-Streaming Agent', 'Final Response')
),
{
name: createTestWorkflowName('E2E - Non-Streaming Workflow'),
tags: ['mcp-integration-test', 'ai-validation', 'e2e']
}
);
const created = await client.createWorkflow(workflow);
context.trackWorkflow(created.id!);
const validationResponse = await handleValidateWorkflow(
{ id: created.id },
repository,
mcpContext
);
expect(validationResponse.success).toBe(true);
const validationData = validationResponse.data as ValidationResponse;
expect(validationData.valid).toBe(true);
expect(validationData.errors).toBeUndefined();
});
// ======================================================================
// TEST 5: Test Node Type Normalization (Bug Fix Validation)
// ======================================================================
it('should correctly normalize node types during validation', async () => {
// This test validates the v2.17.0 fix for node type normalization
const languageModel = createLanguageModelNode('openai', {
name: 'OpenAI Model'
});
const agent = createAIAgentNode({
name: 'AI Agent',
text: 'Test agent'
});
const httpTool = createHTTPRequestToolNode({
name: 'API Tool',
toolDescription: 'Calls external API',
url: 'https://api.example.com/test'
});
const workflow = createAIWorkflow(
[languageModel, agent, httpTool],
mergeConnections(
createAIConnection('OpenAI Model', 'AI Agent', 'ai_languageModel'),
createAIConnection('API Tool', 'AI Agent', 'ai_tool')
),
{
name: createTestWorkflowName('E2E - Type Normalization'),
tags: ['mcp-integration-test', 'ai-validation', 'e2e']
}
);
const created = await client.createWorkflow(workflow);
context.trackWorkflow(created.id!);
const validationResponse = await handleValidateWorkflow(
{ id: created.id },
repository,
mcpContext
);
expect(validationResponse.success).toBe(true);
const validationData = validationResponse.data as ValidationResponse;
// Should be valid - no false "no tools connected" warning
expect(validationData.valid).toBe(true);
// Should NOT have false warnings about tools
if (validationData.warnings) {
const falseToolWarnings = validationData.warnings.filter(w =>
w.message.toLowerCase().includes('no ai_tool') &&
w.nodeName === 'AI Agent'
);
expect(falseToolWarnings.length).toBe(0);
}
});
});